Paper Info
Title
High-Throughput Neuroanatomy and Trigger-Action Programming: A Case Study in Research Automation
Abstract
Exponential increases in data volumes and velocities are overwhelming finite human capabilities. Continued progress in science and engineering demands that we automate a broad spectrum of currently manual research data manipulation tasks, from data transfer and sharing to acquisition, publication, and analysis. These needs are particularly evident in large-scale experimental science, in which researchers are typically granted short periods of instrument time and must maximize experiment efficiency as well as output data quality and accuracy. To address the need for automation, which is pervasive across science and engineering, we present our experiences using Trigger-Action-Programming to automate a real-world scientific workflow. We evaluate our methods by applying them to a neuroanatomy application in which a synchrotron is used to image cm-scale mouse brains with sub-micrometer resolution. In this use case, data is acquired in real-time at the synchrotron and are automatically passed through a complex automation flow that involves reconstruction using HPC resources, human-in-the-loop coordination, and finally data publication and visualization. We describe the lessons learned from these experiences and outline the design for a new research automation platform.
Year
DOI
Venue
2018
10.1145/3217197.3217206
AI-Science@HPDC
Keywords
Field
DocType
Research Automation, Ripple, Neuroanatomy
Data quality,Software engineering,Data transmission,Computer science,Visualization,Automation,Throughput,Data manipulation language,Workflow
Conference
ISBN
Citations 
PageRank 
978-1-4503-5862-0
4
0.45
References 
Authors
0
8
Name
Order
Citations
PageRank
Ryan Chard110512.60
Rafael Vescovi240.45
Ming Du340.45
Hanyu Li465.58
Kyle Chard551556.70
Steven Tuecke64625708.07
Narayanan Kasthuri7627.11
Foster Ian8229382663.24